General fMRI resources
Beckmann, C.F., Jenkinson, M. & Smith, S.M. (2003). General multilevel linear modeling for group analysis in fMRI. NeuroImage, 20: 1053-1063.
Bejsterbosch, J., Smith, S. & Beckmann, C. (2017). Introduction to Resting State fMRI Functional Connectivity. Oxford: Oxford University Press.
Bell, D.J. & Yeung, J. et al. (2019). Signal to noise ratio. Radiopaedia. [Website]. https://radiopaedia.org/articles/signal-to-noise-ratio-1.
Caballero-Gaudes, C. & Reynolds, R.C. (2017). Methods for cleaning the BOLD fMRI signal. NeuroImage. 154: 128-149
Dale, A.M. (1999). Optimal Experiment Design for Event-Related fMRI. Human Brain Mapping, 8: 109-114.
Etzel, J.A., Gazzola, V. & Keysers, C. (2009). An introduction to anatomical ROIbased fMRI classification analysis. Brain Research, 1282: 114-125
Faro, S.H. & Mohamed, F.B. (Eds.) (2010). A Guide to Functional Imaging for Neuroscientists. New York: Springer.
Logothetis, N.K. (2008). What we can do and what we cannot do with fMRI. Nature, 453(12): 869-878.
Wagner, T.D. & Lindquist, M.A. (2015). Principles of fMRI. LeanPub.
Neuroimaging Analysis Software
Bowring, A., Maumet, C. & Nichols, T.E. (2019). Exploring the impact of analysis software on task fMRI results. Human Brain Mapping. 2019; 1-23.
Flandin, G., & Friston, K. J. J. S. (2008). Statistical parametric mapping (SPM). 3(4).
FreesurferWiki. (2020). Freesurfer Wiki. [Website]. https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferWiki
FSL. (2019). The FSL Wiki. [Website]. https://fsl.fmrib.ox.ac.uk/fsl/fslwiki
Henson, R.N. et al. (2019). Multimodal Integration of M/EEG and f/MRI Data in SPM12. Frontiers in Neuroscience, 13: 300.
Jahn, A. (2019b). Andy’s Brain Book. [Website]. https://andysbrainbook.readthedocs.io/en/latest/
Mindhive. (2020). Nutshell SPM. [Website]. Mindhive – A community porthole for MIT brain research. http://mindhive.mit.edu/node/85
National Institutes of Health. (2020). AFNI [Website]. https://afni.nimh.nih.gov/
Nichols., T.E. et al. (2017). Best practices in data analysis and sharing in neuroimaging using MRI. Nature Neuroscience. 20(3): 299-303.
The Wellcome Centre for Human Neuroimaging. (2020). SPM [Website]. https://www.fil.ion.ucl.ac.uk/spm/software/
Behavioral Data Software
Pierce, J. et al. (2019). PsychoPy2: Experiments in behavior made easy. Behavior Research Methods, 51: 195–203.
Psychtoolbox-3. (2020). Psych Toolbox-3 [Website]. http://psychtoolbox.org/
Spapé, M., Verdonschot, R., & van Steenbergen, H. (2019). The E-Primer: An introduction to creating psychological experiments in E-Prime. Second Edition. https://tinyurl.com/y729hhby
Scripting for Analysis
FS Tutorial/Scripts. (2016). Freesurfer Scripts. [Website]. https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/Scripts
Jahn, A. (2012a). AFNI's uber_subject.py. [Website]. Andy's Brain Blog. https://andysbrainblog.blogspot.com/2012/12/afnis-ubersubjectpy.html
Jahn, A. (2012b). FSL Tutorial #6: Automating FEAT. [Video file]. Behavioral Data Software Scripting for Analysis https://www.youtube.com/watch?v=HMXnUkOJDdk
Jahn, A. (2014). Running SPM First-Level Analyses from the Command Line. [Website]. Andy's Brain Blog. https://andysbrainblog.blogspot.com/2014/06/running-spm-first-levelanalyses-from.html
Jahn, A. (2020). SPM Tutorial #6: Scripting. [Video file]. https://www.youtube.com/watch?v=-1m8nriF11I
Muller, E. (2015). Python in neuroscience. Front Neuroinform, 9: 11.
Wallisch et al. (2014). MATLAB for Neuroscientists. 2nd Ed. London: Academic Press.https://tinyurl.com/y7wutosa
Wilson, G. et al. (2017). Good enough practices in scientific computing. PLOS Computational Biology, 13(6): e1005510.
Data Management and Security
Bischoff-Grethe, A. et al. (2007). A Technique for the De-identification of Structural Brain MR Images. Human Brain Mapping, 28(9): 892-903.
Gorgolewski, K. et al. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Nature Scientific Data, 3:160044.
Gorgolewski, K. J. & Poldrack, R.A. (2016). A Practical Guide for Improving Transparency and Reproducibility in Neuroimaging Research. PLOS Biology, 14(7): e1002506.
Gorgolewski, K. et al. (2017). BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methhods. PLOS Computational Biology, 13(3): e1005209.
Schimke, N., Kuehler, M., & Hale, J. (2011). Preserving Privacy in Structural Neuroimages. Data and Applications Security and Privacy. XXV, LNCS 6818: 301- 308.